Faculty Publications
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Item ANN model for prediction of bit–rock interface temperature during rotary drilling of limestone using embedded thermocouple technique(Springer Science and Business Media B.V., 2020) Vijay Kumar, V.K.; Kunar, B.M.; Murthy, C.S.N.In the present work, an artificial neural network (ANN) model has been developed to predict the bit–rock interface temperature using a newly fabricated grounded K-type thermocouple (range 0–1250 °C) during rotary drilling in a CNC vertical machining center. The data have been taken from experimental observation using an embedded thermocouple technique in the laboratory at room temperature (28 °C) using a masonry drill bit. The observations were made using four different operational conditions, namely drill bit diameter (6, 8, 10, 12 and 16 mm), spindle speed (250, 300, 350, 400 and 450 rpm), rate of penetration (2, 4, 6, 8 and 10 mm min?1) and depth (6, 14, 22 and 30 mm). The ANN has been developed based on the multi layer perceptron neural network (MLPNN) with four different input parameters. A Levenberg–Marquardt (LM) algorithm with feed-forward and backward propagation has been used in this model. The predicted value of the bit–rock interface temperature with the highest R2 value provides a satisfactory result with the experimental data. The training value of RMSE is 1.2127, MAPE is 0.0196 and R2 is 0.9960, while the testing value of RMSE is 1.2770, MAPE is 0.0170 and R2 is 0.9978. The ANN model shows that the proposed MLPNN model successfully predicts the bit–rock interface temperature during the rotary drilling of limestone. © 2019, Akadémiai Kiadó, Budapest, Hungary.Item Comparison of modeling methods for wind power prediction: a critical study(Higher Education Press Limited Company, 2020) Shetty, R.P.; Sathyabhama, A.; Pai, P.S.Prediction of power generation of a wind turbine is crucial, which calls for accurate and reliable models. In this work, six different models have been developed based on wind power equation, concept of power curve, response surface methodology (RSM) and artificial neural network (ANN), and the results have been compared. To develop the models based on the concept of power curve, the manufacturer’s power curve, and to develop RSM as well as ANN models, the data collected from supervisory control and data acquisition (SCADA) of a 1.5 MW turbine have been used. In addition to wind speed, the air density, blade pitch angle, rotor speed and wind direction have been considered as input variables for RSM and ANN models. Proper selection of input variables and capability of ANN to map input-output relationships have resulted in an accurate model for wind power prediction in comparison to other methods. © 2018, Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature.Item Numerical simulation and prediction model development of multiple flexible filaments in viscous shear flow using immersed boundary method and artificial neural network techniques(IOP Publishing Ltd custserv@iop.org, 2020) Kanchan, M.; Maniyeri, R.Many chemical and biological systems have applications involving fluid-structure interaction (FSI) of flexible filaments in viscous fluid. The dynamics of single- and multiple-filament interaction are of interest to engineers and biologists working in the area of DNA fragmentation, protein synthesis, polymer segmentation, folding-unfolding analysis of natural and synthetic fibers, etc. To perform numerical simulation of the above-mentioned FSI applications is challenging. In this direction, methods like the immersed boundary method (IBM) have been quite successful. We simulate the dynamics of multiple flexible filaments subjected to planar shear flow at low Reynolds number using the finite volume method-based IBM. The governing continuity and Navier-Stokes equations are solved by the SIMPLE algorithm on a staggered Cartesian grid system. The validation of the developed model is done using previous works. The length of the filament, its bending rigidity and fluid shear rate are taken as parametric variables and numerical simulations are carried out. Viscous flow forcing and fractional contraction terms are incorporated so as to effectively categorize filament motion into various deformation regimes. The effects of tumbling motion on the filament migration and recuperative aspects are studied. The mutual interaction of two filaments placed side by side is thus observed. Finally, an artificial neural network model is developed from the IBM simulation results to predict tumbling counts for different filament parameters. © 2020 The Japan Society of Fluid Mechanics and IOP Publishing Ltd.Item Variation and artificial neural network prediction of profile areas during slant type taper profiling of triangle at different machining parameters on Hastelloy X by wire electric discharge machining(SAGE Publications Ltd, 2020) Manoj, I.V.; Narendranath, S.In the present research work, an in-house developed fixture is used to achieve taper profiles which avoids the disadvantages in convention tapering operation in wire electric discharge machining like wire bend, inaccuracies in taper, insufficient flushing, guide wear etc. A simple triangular profile was machined at 0°, 15° and 30° slant/taper angles. These taper profile areas are investigated for various machining parameters like wire guide distance, corner dwell time, wire offset and cutting speed override. It is observed that as the wire guide distance and cutting speed override increases, the profile area decreases. Whereas in case of wire offset, as offset increases the profile areas also increase. The corner dwell time parameter do not effect on the profile area. The taper profile areas measured highest at 30° followed by 15° and 0° slant angles. This is due to the workpiece placed at different angles during machining with the aid of fixture to obtain taper profile. The taper angle represents the angularity of slant triangular profiles. As the slant angle increases the variation in taper error also increases due to higher wire vibration. An artificial neural network model is developed for the prediction of these areas at a different slant angle. The model is validated experimentally where the errors in prediction ranged from 1% to 9%. In conclusion, it can be noticed that the machining parameters and slant angle influence on profiles irrespective of their dimensions. © IMechE 2020.Item Forecasting the CO2 emissions at the global level: A multilayer artificial neural network modelling(MDPI, 2021) Jena, P.R.; Managi, S.; Majhi, B.Better accuracy in short?term forecasting is required for intermediate planning for the national target to reduce CO2 emissions. High stake climate change conventions need accurate predictions of the future emission growth path of the participating countries to make informed decisions. The current study forecasts the CO2 emissions of the 17 key emitting countries. Unlike previous studies where linear statistical modeling is used to forecast the emissions, we develop a multilayer artificial neural network model to forecast the emissions. This model is a dynamic nonlinear model that helps to obtain optimal weights for the predictors with a high level of prediction accuracy. The model uses the gross domestic product (GDP), urban population ratio, and trade openness, as predictors for CO2 emissions. We observe an average of 96% prediction accuracy among the 17 countries which is much higher than the accuracy of the previous models. Using the optimal weights and available input data the forecasting of CO2 emissions is undertaken. The results show that high emitting countries, such as China, India, Iran, Indonesia, and Saudi Arabia are expected to increase their emissions in the near future. Currently, low emitting countries, such as Brazil, South Africa, Turkey, and South Korea will also tread on a high emission growth path. On the other hand, the USA, Japan, UK, France, Italy, Australia, and Canada will continuously reduce their emissions. These findings will help the countries to engage in climate mitigation and adaptation negotiations. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Item ANN modeling and residual analysis on screening efficiency of coal in vibrating screen(Taylor and Francis Ltd., 2022) Shanmugam, B.K.; Vardhan, H.; Raj, M.G.; Kaza, M.; Sah, R.; Hanumanthappa, H.In this paper, coal screening in vibrating screen was carried out with the size ranges of ?6 mm + 4 mm, ?4 mm + 2 mm, and ?2 mm + 0.5 mm. The vibrating screen was newly designed with flexibility in angle and frequency. The vibrating screen experimentation was carried out by varying screen mesh, angle, and screen frequency. During the screening, the angle was kept constant, and frequency was varied to obtain each size range’s screening efficiency. The experimental results of screening efficiency were evaluated for each size fraction range of coal. The maximum efficiency for screening coal with ?6 mm+4 mm, ?4 mm+2 mm, and ?2 mm+0.5 mm size range obtained was 87.60%, 80.93%, and 62.96%, respectively. Further, the prediction model was developed for each size range using a feed-backward artificial neural network (ANN) to consider the back-propagation error technique. For each screening condition, 10 ANN models were developed with the variation in 1–10 different neurons. ANN has provided mathematical models with a 99.9% regression coefficient for predicting each size range’s screening efficiency. Furthermore, the residuals of each optimal ANN model were analyzed using a normal probability plot and histogram. The ANN model’s accuracy was obtained from the residual analysis by evaluating four different model conditions, i.e., independence, homoscedasticity, normality, and mean error. © 2021 Taylor & Francis Group, LLC.Item A novel EFG meshless-ANN approach for static analysis of FGM plates based on the higher-order theory(Taylor and Francis Ltd., 2024) K P, A.; Swaminathan, K.; Indu, N.; H, S.An Element Free Galerkin (EFG) meshless formulation and solutions using higher order shear deformation theory with nine degrees of freedom for the static analysis of Functionally Graded Material (FGM) plates are provided. This technique estimates the shape function using Moving Least Squares (MLS) method. The proposed method is validated by comparing the present findings with those in the literature. A novel Artificial Neural Network (ANN) model is developed to forecast the deflection of FGM plates within less computational time. Detailed parametric and convergent studies reveal that the proposed EFG solution and the ANN technique are more efficient than their conventional counterparts. The validation and comparison of the generated results in the present investigation with the other analysis methods revealed that the EFG method and ANN model give more accurate results than the FEM and other meshless methods. The current EFG-ANN model reduces computing time by 99.94% when compared to the EFG approach. Also, the accuracy is enhanced using the EFG approach with HSDT9 for the FGM plate. © 2023 Taylor & Francis Group, LLC.Item Stability Analysis of Overburden Dumps over Old Underground Workings Using Artificial Neural Networks(Pleiades Publishing, 2024) Harish, P.; Chandar, K.R.Abstract: Stability of overburden dump slopes is a crucial aspect in designing secure and cost-effective dumps. The Strength Reduction Factor (SRF) serves as a widely used term to assess dump stability. This paper focuses on developing an Artificial Neural Network (ANN) model capable of predicting SRF for overburden dumps situated above existing underground workings. To construct the model, a dataset comprising 96 numerical simulations of overburden dumps generated through the finite element method was utilized. A neural network architecture with three layers of forward-backward propagation was utilized, containing hidden neurons to analyze simulations during training, validation and testing stages. The input parameters for studying overburden dump slopes over underground workings included dump slope height (Sh), dump slope angle (), cohesion (C), friction angle (Ø), unit weight () of the dump material, depth of working from the surface (D), centre-to-centre pillar distance in underground workings (C-C), and gallery width (Gw). The ANN predicted results were compared with the outcomes derived from numerical simulations of overburden dump slopes above underground workings. The study highlights that the developed ANN model in this research proves highly effective in handling and designing complex overburden dump slopes. The obtained results indicate a Mean Square Error (MSE) of 0.0595 and a coefficient of determination (R) of 0.883, both of which are considered acceptable. © Pleiades Publishing, Ltd. 2024.Item Experimental and computational studies on characteristics of indigenously produced novel Aegle marmelos micro polymer reinforced aluminum composites using powder metallurgy(Korean Society of Mechanical Engineers, 2025) Veeranaath, V.; Sahu, R.K.; Priya, I.M.In recent times, the quest for an advanced composite material (polymer reinforcement in metal matrix) has become a challenge for promising industrial and household applications. Therefore, the present study focuses on the indigenous production of a novel microparticle-based Aegle marmelos natural polymer reinforced (AMNPR) aluminum composites using the powder metallurgy (P/M) technique. The results revealed that the reinforcement (AMNP) concentration had a considerable effect on the physico-mechanical, thermal, and chemical characteristics of composites. Further, in this study, TOPSIS coupled with the CRITIC method (CRITIC-TOPSIS) is adopted to convert the multiple characteristics into a closeness coefficient (Ci) response. The optimal parameters are found to be reinforcement - 20 wt. %, ball milling duration - 180 min, and speed - 300 rpm. Moreover, the Ci values predicted by the artificial neural network (ANN) model are in good agreement with the experimental values having a mean absolute error of 4.116 %. © The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2025.Item An Artificial Neural Network-Based Approach to Predict Blast-Induced Ground Vibrations in Open Cast Coal Mine— A Case Study(Pleiades Publishing, 2025) Ravikumar, A.; Vardhan, H.; Shankar, M.U.Abstract: This study aims to assess and predict blast-induced ground vibrations of opencast coal mine. The analysis was carried out using two methods i.e. the widely employed empirical vibration predictor known as the USBM (United States Bureau of Mines) equation, and a machine learning model called the artificial neural network (ANN). A dataset including 38 blast vibration recordings was collected and used for the development of an ANN model. Additionally, these datasets were employed to evaluate the site determination constants of the empirical vibration predictor. A total of 27 recordings of blast-induced ground vibrations were gathered from the same opencast coal mine in order to assess the effectiveness of both models. The output (dependent variable) for both models is the peak particle velocity. The effectiveness of the prediction model was evaluated by using commonly used statistical measures, namely the coefficient of determination (). Consequently, the ANN model that was built exhibited more precision in comparison to the existing empirical model. The ANN model exhibited a strong positive relationship between the observed and anticipated peak particle velocity values, as shown by the coefficient of determination (). © Pleiades Publishing, Ltd. 2025.